ConveRT-pytorch | ConveRT Paper Pytorch Implementation | Natural Language Processing library

by codertimo Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | ConveRT-pytorch Summary

ConveRT-pytorch is a Python library typically used in Artificial Intelligence, Natural Language Processing, Pytorch applications. ConveRT-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

ConveRT Paper Pytorch Implementation

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Quality

Security

License

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Support

ConveRT-pytorch has a low active ecosystem.

It has 37 star(s) with 7 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 8 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ConveRT-pytorch is current.

Quality

ConveRT-pytorch has 0 bugs and 0 code smells.

Security

ConveRT-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ConveRT-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ConveRT-pytorch is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

ConveRT-pytorch releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

ConveRT-pytorch saves you 302 person hours of effort in developing the same functionality from scratch.

It has 728 lines of code, 63 functions and 13 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ConveRT-pytorch and discovered the below as its top functions. This is intended to give you an instant insight into ConveRT-pytorch implemented functionality, and help decide if they suit your requirements.

Runs ConveRT training .
Evaluate the model .
Compute the cosine similarity between two contexts .
Calculate the best matching score matching the query .
Initialize the model .
Setup the logger .
Convert encoder to encoder feature .
Create an encoder for batching input features .
Loads datasets from tsv file .
Load examples from reddit dataset .

Get all kandi verified functions for this library.

ConveRT-pytorch Key Features

No Key Features are available at this moment for ConveRT-pytorch.

ConveRT-pytorch Examples and Code Snippets

No Code Snippets are available at this moment for ConveRT-pytorch.

Community Discussions

Trending Discussions on ConveRT-pytorch

Convert CUDA tensor to NumPy

QUESTION

Convert CUDA tensor to NumPy

Asked 2019-Nov-15 at 12:12

First of all, I tried those solutions: 1, 2, 3, and 4, but did not work for me.

After training and testing the neural network, I am trying to show some examples to verify my work. I named the method predict which I pass the image to it to predict for which class it belongs:

...

ANSWER

Answered 2019-Sep-23 at 22:36

Try to change

Source https://stackoverflow.com/questions/57832423

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ConveRT-pytorch

You can download it from GitHub.
You can use ConveRT-pytorch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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